IA2former: Illumination-Aware Attention-based Transformer for Low-light Image Enhancement

Abstract

Low-light image enhancement has made significant progress through both traditional Retinex methods and deep learning techniques. Traditional Retinex-based methods decompose images into illumination and reflectance components to mimic human perception of brightness and color. However, these methods often struggle with noise suppression and detail preservation, particularly under severe low-light conditions. Recent Transformer-based methods, such as RetinexFormer and Restormer, have improved restoration performance by modeling long-range dependencies, but they still insufficiently explore the interaction between illumination variations and spatial--semantic features. To address these limitations, we propose Illumination-Aware Attention-based Transformer (IA2former), a novel low-light image enhancement model that explicitly models illumination-aware feature interactions. By integrating an Illumination-Aware Attention mechanism and an Illumination-Aware Loss function, IA2former effectively captures long-range dependencies, improves detail restoration, and preserves spatial structures under challenging illumination conditions. Experimental evaluations on the LOL-v1 and LOL-v2 datasets demonstrate that IA2former achieves a favorable overall balance across PSNR, SSIM, and LPIPS, obtaining the best performance on multiple metrics and remaining competitive on others. These results validate the effectiveness and robustness of the proposed illumination-aware modeling strategy for low-light image enhancement.